Forgery Localization Based on Image Chroma Feature Extraction

Alfraih, Areej S.; Briffa, Johann A.; Wesemeyer, Stephan

doi:10.1049/ic.2013.0279

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Fig. 11 shows the comparison graph for SPS-SIFT-IFD and existing system [16,17]. In that the accuracy, sensitivity, specificity, precision, F_Measure and Gmean value are increased, recall, value is decreased.…”

Section: Specificitymentioning

confidence: 98%

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A Novel Method of Scale-Invariant Feature Transform Based Image Forgery Detection

Mahesh¹,

Maloji²,

Rao³

2018

IJIES

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Now a days Image forgery (IF) makes many problems in a society. Super Pixels Segmentation and Scale-Invariant Feature Transform based Image Forgery Detection (SPS-SIFT-IFD) has introduced to detect the forgeries in the images. The forgery region extraction algorithm replaces the features point with small SP as feature blocks and neighboring blocks that have similar local color features has been identified by the Scale-Invariant Feature Transform (SIFT) technique. Artificial Neural Network (ANN) classifier is used for better detection of forged parts in the original images. It applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the SPS-SIFT-IFD scheme achieve much better detection results even under various challenging conditions compared with the existing Forgery Detection (FD) methods. Finally, SPS-SIFT-IFD method performance is measure, in terms of Recall, Precision, False Measure, Sensitivity, Sensitivity, Accuracy and Gmean.

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Section: Specificitymentioning

confidence: 98%

“…Sensitivity is a basic property of image Figure.11 The result and comparison of SPS-SIFT-IFD and existing system [16,17] processing the. Sensitivity is also called as rate.…”

Section: Sensitivitymentioning

confidence: 99%

A Novel Method of Scale-Invariant Feature Transform Based Image Forgery Detection

Mahesh¹,

Maloji²,

Rao³

2018

IJIES

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“…To evaluate this probability, it may be useful to have a confidence interval, rather than a single point estimate. For this reason, authors such as [52] study the confidence intervals of the probabilistic classification.…”

Section: A Forgery Localization and Forgery Maskmentioning

confidence: 99%

Detecting Image Brush Editing Using the Discarded Coefficients and Intentions

López¹,

de-la-Fuente-Valentín²,

Sarría³

2019

IJIMAI

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This paper describes a quick and simple method to detect brush editing in JPEG images. The novelty of the proposed method is based on detecting the discarded coefficients during the quantization of the image. Another novelty of this paper is the development of a subjective metric named intentions. The method directly analyzes the allegedly tampered image and generates a forgery mask indicating forgery evidence for each image block. The experiments show that our method works especially well in detecting brush strokes, and it works reasonably well with added captions and image splicing. However, the method is less effective detecting copy-moved and blurred regions. This means that our method can effectively contribute to implementing a complete imagetampering detection tool. The editing operations for which our method is less effective can be complemented with methods more adequate to detect them.

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Forgery Localization Based on Image Chroma Feature Extraction

Cited by 2 publications

References 15 publications

A Novel Method of Scale-Invariant Feature Transform Based Image Forgery Detection

A Novel Method of Scale-Invariant Feature Transform Based Image Forgery Detection

Detecting Image Brush Editing Using the Discarded Coefficients and Intentions

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